Journal Article

Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising

Grussu, Francesco, Cohen-Adad, Julien, Shepherd, Timothy M., Schneider, Torben, Alexander, Daniel C., Veraart, Jelle, Novikov, Dmitry S., Battiston, Marco, Fieremans, Els, Gandini Wheeler-Kingshott, Claudia A.M.

2020

Overview

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA. •We present a multi-parametric MRI protocol for in vivo spinal cord microstructural imaging based on a unified signal readout.•The protocol enables the evaluation of diffusion, relaxation and myelin metrics with matched resolution and distortions.•The unified readout enables multi-contrast analyses, which are demonstrated by multi-contrast MP-PCA denoising.•Simulations and multi-vendor in vivo data show that MP-PCA is a useful pre-processing step for spinal cord imaging pipelines.•The performance of MP-PCA greatly benefits from the increased number of measurements enabled by the unified readout.

• Publisher: Elsevier Inc,Elsevier Limited,Academic Press,Elsevier
• Subject: Algorithms / Computer Simulation / Decomposition / Diffusion Tensor Imaging / Echo-Planar Imaging - instrumentation / Echo-Planar Imaging - methods / Humans / Image Enhancement / Image Interpretation, Computer-Assisted / Magnetic resonance imaging / Marchenko-Pastur PCA denoising / Multi-parametric MRI / Myelin / Myelin Sheath - pathology / Neurological diseases / Noise / Physiology / Principal Component Analysis / Principal components analysis / Quality / Quantitative MRI / Signal readout / Signal-To-Noise Ratio / Software / Spinal cord / Spinal Cord - diagnostic imaging